Political Mobilization Through Online Social Networks

Elizabeth A. G. Schwarz University of California, Riverside

Word count: 9212

18 July 2011

Direct correspondences to: Elizabeth A. G. Schwarz, University of California, Riverside, Sociology Department, 900 University Ave., Riverside, CA 92521; eschw001@ucr.edu.

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Abstract The recent revolutions in the Middle East brought attention to the use of online social networks

for social movement mobilization. Using data from a survey of participants fielded at the U.S.

Social Forum (USSF), this analysis provides a comparison of mobilization through online social

networks with face-to–face and mediated communication channels. Specifically, the research

examines online social networks in regard to offline protest activity and organizational

membership diversity, or the number of types of organizations with which individuals are

affiliated. It was found that mobilization to attend the USSF through online social networks

significantly impacts organizational membership diversity and the number of offline protests

attended. Activists should use online social networks to supplement more traditional modes of

mobilization.

Keywords: social movement, Internet, protest, online social network, united states social forum

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Introduction

The Middle East revolutions in early 2011 set off widespread speculation about the role

of the Internet, and particularly social media tools such as Facebook and Twitter, in facilitating

social movement activity (Mejias 2011). On February 5, 2011 a New York Times article

headline announced, “Facebook and YouTube Fuel the Egyptian Protests” (Preston 2011). A

February 1, 2011 CNN.com article headline proclaimed, “Google, Twitter, help give voice to

Egyptians” (Gross 2011). However, not everyone holds such enthusiastic views of social media

and instead downplay social media’s role in the revolutions (Mejias 2011). Demonstrating a

more moderate view, recent writings on the Middle East revolutions place the accomplishments

of the revolutions squarely on the shoulders of the people of the Middle East while arguing that

social media tools are important as well (Tufekci 2011; Zhuo, Wellman, and Yu 2011). Tufekci

(2010) emphasizes that developing an understanding of the role social media tools play in

protests requires a focus on the operation of networks and examinations of how to sustain the

participatory, non-hierarchical environment often created by social media.

Many questions remain regarding how networking occurs online and what types of

movements and organization are poised to best make use of such networking. The importance of

social networks is well established in social movement mobilization literature, revealing the

impact of personal and organizational connections on engaging in political and civic activities

(e.g., Snow, Zurcher, and Ekland-Olson 1980, McAdam 1986, McAdam and Paulsen 1993, Kitts

2000, Passy and Giugni 2001). A large number of researchers have also focused on the influence

of the Internet, in general, on social movement activity (e.g., Diani 2000, Wellman 2002, della

Porta and Mosca 2005, Fisher and Boekkoi 2010). In addition, many researchers call attention to

the emergence of social movements that are built on non-hierarchical, diversely networked bases

(e.g.; Castells 1996, Ronfeldt andArquilla 2001, Castells 2004, Juris 2004, Bennett et. al. 2008).

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The social forum process is one illustration of an environment that was formed with a

goal of creating a participatory and non-hierarchical space for social movement organization

(Bennett et. al. 2008, Reese et. al. 2011). Byrd and Jasny (2010) identify the social forum process

as an example of organization based on networks and argue that, “the manner in which

organizations, networks, and coalitions engage the Forum, interact with other movements and

organizations, and frame their collaborative proposals reveals important processes with empirical

implications in the field of social movements and organizational theory” (p. 357). The United

States Social Forum (USSF), a Forum located in the United States that brings together

individuals from a variety of groups and movements under the umbrella of the global social

justice (GSJ) movement, is one such Forum. One of the goals of the USSF is to “Build stronger

relationships and collaboration between movements” (www.ussf2010.org/about 2010). One

method the USSF used to mobilize participants toward this goal was the use of online social

networks. As of July 18, 2011 the USSF had 2,824 followers and was listed 195 times on Twitter

(Twitter 2011). 16926 users liked the USSF fan page on Facebook (Facebook 2011).

This study uses survey data from the USSF to answer Polat (2005) and Kavada’s (2010)

call for research that examines different facets of the Internet by examining whether online social

networks increase organizational membership diversity, or the number of organizations with

which individuals are affiliated, and offline protest activity. I thereby extend the research on the

Internet, social movement mobilization, and networking to include the impact of online social

networks by comparing its impact on mobilization outcomes to that of face-to-face and mediated

forms of mobilizing structures. The findings suggest that online social networks have a

significantly positive impact on the two mobilization outcomes examined that is larger than

traditional modes of movement mobilization, even when controlling for individual factors. As

social movements continue to increase their use of online social networks to mobilize

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participants, the knowledge of these individuals’ characteristics will be vital to social movement

organizations and processes like the social forums.

Moving Networks Online

Global Justice Movement

The World Social Forum (WSF) is described as an attempt to bring many otherwise

globally dispersed activists and intellectuals together, including myriad organizations, from non-

governmental organizations (NGOs) and international non-governmental organizations (INGOs)

to advocacy groups and social movement organizations (SMOs), as part of the GSJ movement

(Langman 2005). According to the World Social Forum Charter of Principles, the Forum is, “an

open meeting place for reflective thinking, democratic debate of ideas, formulation of proposals,

free exchange of experiences and interlinking for effective action” (World Social Forum 2002).

The GSJ movement and social forum processes have been recognized as examples of

loosely networked structures that promote inclusiveness and diversity of individuals and causes

(Bennett et. al. 2008). The goal of the GSJ movement is to bring together various movements in

order to create a more just world (Reese et al. 2011). The USSF and other regional and topical

forums developed in the same spirit as the WSF (Reese et al. 2011). In Detroit, Michigan in June

2010 approximately 20,000 activists, representing a variety of organizations and social

movements, gathered together in the largest meeting of progressive social justice movement

activists in the U.S.

The GSJ movement is identified as an ideal movement for citizens to make use of the

Internet to organize and mobilize (Castells 2004). Specifically, Castells (2004) points to the

Zapatistas’ movement in Mexico in the 1990s as one of the first informational guerrilla

movements. Castells explains,

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Extensive use of the Internet allowed the Zapatistas to diffuse information and their call

throughout the world instantly, and to create a network of support groups, which helped

to produce a movement of international public opinion that made it literally impossible

for the Mexican government to use repression on a large scale. (p. 84)

Ronfeldt and Arquilla (2001) also point to the Zapatistas’ movement as an example of the

concept of what they coined ‘netwars,’ which they argue develops from the rise of the

information revolution. Within a society in which netwars emerge, network forms of

organizations have advantages over hierarchical forms of organizations. In addition, what

transpires from conflicts in large part depends on effective use of information and

communication. They agree with Castells that information played an important role in the

Zapatistas’ movement and point out the importance of such movements learning how to develop

their own “cultural codes” (p. 191) and propagating those codes throughout society.

Networking

Communication is important to social movement recruitment (Snow, Zurcher, and Eland-

Olson 1980). Communication is typically broken up into face-to-face communication, or ‘all

information, whether it be verbal or nonverbal, that is imparted when two or more individuals or

groups are physically present,’ and mediated communication, or ‘information dissemination

through institutionalized mass communication mechanisms, such as radio and television, or

through institutionalized, but individualized and privatized, communication mechanisms such as

the mail and telephone’ (ibid). Diani (2000) further expands the categories of communication to

four: private and direct, private and mediated, public and direct, and public and mediated. He

asserts that what has been coined computer-mediated communication (CMC) does not fall neatly

into the conventional typologies of communication described above. Nonetheless, the

connections between individuals made over the Internet are considered to be another form of

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social networks and as such should be included when examining mobilization to take part in

social movement activity (Wellman 2001).

Traditional network theory examines the impact of networks created through

interpersonal and organizational ties. Larger social pattern emerge through social networks and

interactions between individuals (Granovetter 1973). Networks play integral roles in behavior

change, such as smoking cessation (Christakis and Fowler 2008). Social networks are also

important to emotion dispersion, such as happiness (Fowler and Christakis 2008).

Similar results are found the in social movement mobilization literature, demonstrating

the influence of personal and organizational connections on engaging in activism (Snow,

Zurcher, and Ekland-Olson 1980, McAdam 1986, McAdam and Paulsen 1993, Kitts 2000, Passy

and Giugni 2001). Interpersonal ties or informal networks have been identified as primary

motivators for individuals to join movements. People are much more likely to participate in

movement activity if they have a connection to someone already in the movement (Snow et. al.

1980). In addition, people’s interests in certain topics increase when they engage with

individuals who have interests similar to their own (Kitts 2000). Research looking at ties across

movements demonstrates how, in certain cases, those ties can lead to common viewpoints,

shared identities, and collective action (Carroll and Ratner 1996).

Tie strength, or “the combination of the amount of time, the emotional intensity, the

intimacy (mutual confiding), and the reciprocal services which characterize the tie,” also impact

networks (Granovetter 1973: 1361). Strong ties, such as ties between close friends or family,

offer stronger social incentives to participate in social movement activity and consequently are

more effective recruitment channels than weak ties, like those ties with friends of friends

(McAdam 1986). However, weak ties are still important as they can act as bridges between

groups and offer access to information and resources that family and immediate friends may not

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provide (Granovetter 1983). Network ties developed using the Internet are considered to be weak

ties (Donath and boyd 2004, Haythornthwaite 2005). Weak ties can help social movements

facilitate communication, collective organization and action (Kavanaugh, Reese, Carroll, and

Rosson 2005).

Organizational ties are also central to social movement mobilization. Being part of

multiple movements and organizations can help information, resources, and expertise flows more

freely between movements and organizations when people are affiliated with multiple

movements and organizations. Affiliation with organizations is one of the strongest predictors of

participation in social movement activities (McAdam 1986, McAdam & Paulsen 1993).

Research shows organizational ties are often more important to participants than individual ties

when they decide to engage in social movement activity (McAdam and Paulsen 1993). In

support of this argument, recent research finds social movement organizations play a significant

role in mobilizing and supporting participation in large-scale protests (Fisher, Stanley, Berman,

and Neff 2005).

On the topic of organization and recruiting strategies, Juris (2004) argues contemporary

social movements share the following principles: “1) forging horizontal ties and connections

among diverse, autonomous elements; 2) the free and open circulation of information; 3)

collaboration through decentralized coordination and consensus decision-making; and 4) self-

directed networking.” These principles are adopted by activists and ultimately end up influencing

networking practices. Embracing these principles changes the goal of movements from recruiting

activists to their particular movement to, “horizontal expansion through articulating diverse

movements within flexible structures that facilitate maximal coordination and communication”

(Juris 2005).

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The introduction of the Internet, and the speed and proliferation of information spread

across the Web by individual actors, may lead participants to aspire to have increasingly flexible

relationships with organizations, which may change the role that organizations play in social

movement mobilization (Bennett et. al. 2008). More recently, research has shown that Internet

users belong to increased numbers of organizations (Bennett et. al. 2008, Van Laer 2010). The

number of types of organizations people are affiliated with has been coined organizational

membership diversity (Bennett et. al. 2008). For the purpose of this study, organizational

membership diversity will be an indicator of participants’ interpersonal network ties.

The Internet, Civic Engagement, and Collective Action

Researchers identify the Internet as playing a key role in shaping political and cultural

life (Kahn and Kellner 2004). Castells (1996) asserts that CMC and other mediated social

networks have transformed society into a networked society where information exchange is

instantaneous and global. The Internet society is less constrained by geographic location than

previous societies (Hugill 1999). Wellman (2002) argues that, in part from the introduction of the

Internet, the nature of social relationships have shifted toward networked individualism. With

this shift, he theorizes, individuals have multiple and shifting work partners and partial

involvement with shifting set of workgroups that are not based on location, but rather based on

the network ties of the individual. In addition, many contacts initiated through online social

networks transition to offline meetings. Research suggests most Internet users make use of the

Internet to extend their offline participation in various activities (Wellman, Haase, Witte, and

Hampton 2001).

Scholars agree that the Internet impacts civic engagement and social movement activity

(Diani 2000, Wellman 2002, della Porta and Mosca 2005, Fisher and Boekkoi 2010). A study of

National Geographic readers reveals the Internet supplements and increases their organizational

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and political involvement (Wellman, et. al. 2001). Shah, Kwak, and Holbert (2001a) find

peoples’ uses of the Internet for information exchange relate to increased offline civic

engagement. Internet use patterns more strongly influence civic participation than traditional

media (Shah, McLeod, Yoon 2001b). In addition, increased time spent on the Internet positively

relates to civic engagement (Shah, Schmierbach, Hawkins, Espino, and Donavan 2002). More

recent research demonstrates that online information seeking and interactive civic messaging

more strongly influence offline civic engagement than traditional print and broadcast media and

face-to-face communication (Shah, Cho, Evelands, and Kwak 2005).

della Porta and Mosca (2005) identify three contributions the Internet brings to collective

action: (1) organization, logistics, and networking between groups, (2) a way of expressing

dissent and protest, and (3) information dissemination. Similarly, research on the impact of the

Internet on collective action overall reveals the Internet influences social movement mobilizing

structures, opportunity structures, and framing processes (Garrett 2006).

In addition, the Internet offers social movements the speed and range of communication

that technologies, such as printing, the postal system, the telephone, and fax did in the past (della

Porta and Mosca 2005). Use of the Internet may also increase the accuracy of messaging and

interaction between organizations and activists (Diani 2000). Social movement participants can

use the Internet to spread uncensored messages and impact the mass media (della Porta and

Mosca 2005) The Internet provides hyperlinked communication networks that enable individuals

to find multiple points of entry into varieties of political action and offers independence from the

mass media and other conventional institution organizations (Bennett 2003, Castells 2004,

Bennett et al. 2008). Furthermore, use of the Internet facilitates permanent social movement

campaigns, the growth of broad social movement networks, and the transformation of individual

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member social movement organizations and growth patterns of whole social movement networks

(Bennett 2003).

The Internet also facilitates online social movement activism and protest (Ronfeldt and

Arquilla 2001, Brunsting and Postmes 2002, Marmura 2008, Earl and Kimport 2008). The

Internet, as a platform for action, is most appropriate for soft collective actions, or actions that

are used to persuade others of a certain viewpoint rather than engage and confront another party

more directly (Brunsting and Postmes 2002). Earl (2006) explores four online activist tactics:

online petitioning, boycotting, and emailing and letter-writing campaigns. Recent research also

explores the role of the Internet in movement building by investigating websites of movement

organizations. This includes research that uses content analysis of sites and examinations of

cross-linking of websites (Van Aelst and Walgrave 2002, Huey 2005, Reid and Chen 2007, Stein

2009).

One of the most well documented areas of research concerning the Internet and collective

action is the relationship between online and offline collective action (Brunsting and Postmes

2002a, Brunsting and Postmes 2002b, Kahn and Kellner 2004, Reid and Chen 2007, Wojcieszak

2009). Offline and online protests are strongly related and tend to reinforce each other (della

Porta and Mosca 2005). The Internet is a place where otherwise isolated, distant individuals and

networks can come together and work toward forms of collective action (Ibid). Most recently,

Fisher and Boekkooi (2010) find the Internet plays a major role in mobilizing participants for

global days of action.

There is also a body of research that examines various aspects of the Internet at prior

Forums. From a survey fielded at the Genoa European Social Forum (ESF), della Porta and

Mosca (2005) uncover positive relationships between online and offline protests and positive

relationships between Internet use and multiple memberships in organizations. Recent research

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explores the role of media communication, including the Internet, in mobilizing participants at

the ESF (Kavada 2005). Email lists are found to play a role in the organizing process of and

decision making in the London 2004 ESF (Kavada 2010).

While the beneficial impacts of the Internet have been extolled, there is also concern that

the digital divide impacts who has access to the Internet as well as who has the ability to use it

(Castells 1996, Hugill 1999, Castells 2004, Martin and Robinson 2007, Goldfarb and Prince

2008). Social movement scholars contend there is an increased digital divide between individuals

who are more politically active and those who are less active based on socioeconomic status,

age, and prior political participation which may reinforce current political activity in society

(Van Laer 2010, Van Laer and Van Aelst 2010). Research examining Internet and civic skills

finds that high socioeconomic status individuals are more likely to receive mobilization

messages online and offline (Best and Krueger 2005).

At the time of these studies online social networks were not as prevalent as they are now

and therefore these studies did not specifically concentrate on online social networks. Online

social network websites are unique to the Internet because they allow users to make their social

networks visible to other users (boyd & Ellison, 2007). Scholars call for research that examines

different facets of the Internet, including research specifically focused on exploring online social

networks (Polat 2005, Kavada 2010).

Online Social Networks

Although the first online social network site launched in 1997, social movement research

specifically focusing on online social networks, such as Facebook, MySpace, YouTube, and

Twitter, is not as robust as research focused on the Internet in general. Differing from traditional

websites, online social network sites are ‘‘web-based services that allow individuals to (1)

construct a public or semipublic profile within a bounded system, (2) articulate a list of other

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users with whom they share a connection, and (3) view and traverse their list of connections and

those made by others within the system’’ (boyd & Ellison, 2007). However, similar to other

forms of Internet tools, most often, online social network sites are used to support existing

offline social relations and activities (Ibid).

Recent research examining online social networks shows support for increased civic

engagement by young online social network users (Pasek, More, and Romer 2009). Online social

networks are described as websites that are ideal for encouraging interpersonal interaction,

broadening social ties, and providing valuable information about how to become civically and

politically involved. Current research demonstrates blogging and online social networks have

positive relationships with participation in civic organizations (Valenzuela, Park, and Kee 2009).

Examining the role online social networks played in the 2008 Presidential election, results show

a positive relationship between online social network use and civic participation (Zhang, Seltzer,

and Bichard 2010).

A study of young users of the online social network Facebook reveals mixed findings.

While there is a positive relationship between the use of Facebook for political purposes and

general political participation, there is a negative relationship between increased Facebook use

and general political participation. While the researchers acknowledge this result is difficult to

explain, they suggest users may be using Facebook to supplement political activity in other

venues (Vitak, Zube, Smock, Carr, Ellison, and Lampe 2010). The research presented in this

paper expands the existing online social network research into the area of social movement

activity. It is proposed:

H1: Mobilization to attend the USSF through online social networks impacts

organizational membership diversity.

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H2: Mobilization to attend the USSF through online social networks impacts the number

of offline protests attended.

As mentioned in previous studies focused on topics surrounding the GSJ movement, the

hypotheses presented in this research may not hold true for all types of movements. The GSJ

movement covers broad bases of protest issues and draws individuals with broad interests. The

hypotheses presented in this paper may not be as applicable to those movements that are based

on more hierarchical organizational structures with very narrow focuses.

Methods

Data

Data were collected through a written survey of 569 adult participants at the 2010 United

States Social Forum from June 22-26, 2010 in Detroit, Michigan. Historically, surveys have been

shown to be effective tools for examining social movement activity (Bedoyan et al. 2004;

Bennett, Breunig, and Givens 2008; Fisher et al. 2005; Fisher and Boekkooi 2010). The 50-

question survey gathered information about respondents’ demographic and socio-economic

characteristics, political views, affiliations with organizations and social movements, and

political activities.

The sampling frame included participants at the USSF. Researchers acknowledge the

difficulty of sampling at such events (Kavada 2005, Bennett et. al. 2008). A full list of

participants was unavailable at the start of the USSF and the length of the survey required

respondents to spend 30 minutes completing it. Because of these factors a convenience sampling

method was used and as many surveys as could be collected were, at a variety of event venues

including registration, the lobby area, workshops, evening plenaries, organizations’ tables, and

cultural performances. This method is consistent with other survey research projects fielded at

previous social forums (Kavada 2005). To help verify the representativeness of our sample, a

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comparison was made with another academic survey fielded at the USSF, which revealed

comparable demographic results.

Despite best efforts to obtain a representative sample, it is likely that certain sampling

biases resulted. Participants with fewer responsibilities and more free time may have been

oversampled. The attendees who could not read, were not literate in Spanish or English, or those

who were uncomfortable completing written surveys may have been under sampled.

Variables and Measurement

The first dependent variable I analyze is organizational membership diversity, which

reflects the number of organizations of which individuals were members (Bennett et. al. 2008).

Responding to a question inquiring about the types of organizations respondents were members

of, respondents indicated which types of organizations they were affiliated with by checking

responses that included: “Labor union/organizations; Non-governmental organizations;

Government agencies; Cultural groups; Professional associations; Political parties; Media

organizations; Social or recreational groups; Religious institutions/movements; Social

movements/political organizations; or Other.” In order to create a single variable, I first summed

the number of organizational types for each individual. Then, I dichotomized the variable using

the median of the summed value, which equaled two, as the point at which the variable was split

into 0 (equal to or less than two types of organizations) or 1 (greater than 2 types of

organizations).

The second dependent variable is protests, which measures the degree of movement

activism. Responding to an open-ended question, respondents self-reported the number of public

protests or demonstrations they participated in during the last 12 months. Protests is a continuous

variable.

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In each model the same key independent variables and control variables were used. The

variable mobilization channels was created to capture how participants found out about the 2010

USSF. Responding to a question inquiring as to how participants found out about the 2010

USSF, respondents were offered the following responses: “Radio or television; Newspapers

(print or online); Alternative online media; Advertisement, flyers, and/or posters; Family

member and/or partner; Friends and/or acquaintances; People at your school or work; Fellow

members of an organization or association; or Online social networks (e.g. Facebook, Twitter).”

I separated the variable mobilization channels into three variables, online social networks,

mediated, and face-to-face, similar to the categories created by Fisher and Boekkooi (2010) and

Van Laer (2010).

Online social networks is the key independent variable. Responding to a question

inquiring as to how participants found out about the 2010 USSF, respondents who heard about

the USSF through online social networks indicated so by checking the response “Online social

networks (e.g. Facebook, Twitter).” These participants may also have selected other responses

available for that question. The variable online social network was dichotomous for which 1

indicated “Online social networks” was selected and 0 indicated that “Online social networks”

was not selected.

To establish the additional impact of online social networks, mediated and face-to-face

variables were used to control for the influence of other mobilization channels. First, the variable

mediated was created. Responding to a question inquiring as to how participants found out about

the 2010 USSF, respondents who heard about the USSF through mediated channels indicated so

by checking any of the following responses: “Radio or television, Newspapers (print or online),

Alternative online media, Advertisement, flyers, and/or posters.” Mediated was a dichotomous

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variable for which 1 indicated one of the mediated responses was checked and 0 indicated no

mediated responses were checked.

The dichotomous variable face-to-face was also created using responses from the

previously referenced question. Face-to-face was coded 1 if the respondents checked any of the

following responses: “Family member and/or partner, Friends and/or acquaintances, People at

your school or work, or Fellow members of an organization or association.” Otherwise, face-to-

face was coded 0. Because respondents could select more than one entry for this question, it was

possible for one observation to have multiple affirmative values for the face-to-face, mediated

and online social networking variables.

In order to isolate the impact of various mobilization channels on organizational

membership diversity and protest activity, it is important to control for a number of individual

factors that have been shown to predict them. First, age has been shown to influence Internet use

and activism (Van Laer 2010, Best and Krueger 2005, Schussman and Soule 2005). Therefore, to

ensure age did not influence the outcomes, age was used as a control variable. Responding to an

open-ended question inquiring as to the year the respondent was born, respondents self-reported

the year in which they were born. Year was then converted to the age of the respondent for the

purpose of analysis using SAS. Age is a continuous variable.

Next, to address additional issues surrounding the digital divide and demographic

influences of protest activity, gender, race, and personal income were also used as control

variables. Responding to a question inquiring as to their gender, respondents selected “Female,

Male, or Other.” People who don’t identify with one particular gender category or don’t adhere

to gender categorization selected “Other.” Gender was a categorical variable. Responding to a

question inquiring as to their race, respondents selected their race. Options included: “Black,

Middle Eastern, South Asian, East Asian, Island Pacific, Indigenous, Latino/Hispanic, White,

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Multiracial and Other.” Because of limited numbers of observations, South Asian, East Asian,

and Island Pacific were collapsed into the response Asian. Race was a categorical variable.

Responding to a question inquiring about their approximate annual personal income, respondents

selected the category in which their approximate annual income fell. Responses included “None-

$14,999; $15,000-$20,999; $21,000-$39,999; $40,000-$51,999; $52,000-$63,999; $64,000-

$100,000; or Above $100,000.” Because of limited numbers of observations, the last two

response options were collapsed into the response $64,000 or above. Personal income was a

categorical variable. In my model, I used female, None-$14,999, and white as the reference

group for the gender, race, and personal income variables, respectively.

Table one contains descriptive statistics for the variables. Twenty-four percent of the

sample found out about the social forum using online social networks. Eighty-eight percent of

the sample learned about the forum through face-to-face communication whereas 41% of the

sample learned of the forum through mediated channels. The highest percentage of participants

has income levels lower than $14,999. Skewness was used to examine how close to normal the

data are for the continuous variables. The skewness for protests is 5.34. This indicates the

distribution for protest is not normal. The skewness for age is .97, which indicates it has a normal

distribution. The remaining variables are not continuous. An alpha level of .05 was used in the

analyses.

Table 1 about here

Statistical Estimation

In order to test the first hypothesis that mobilization through online social networks

impacts organizational membership diversity, model one uses logistical regression. Logistic

regressions allow researchers to “…model a categorical dependent variable as a function of a set

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of explanatory variables…” (Demaris 1992 p. 1). The logistic regression equation for the log

odds of Y is:

Log Odds(Y=1) = β 0 + β 1X1 + β 2X2 + β 3X3 …+ β KXK

Logistic regression is an appropriate test because this research investigates if the discrete

dependent variable higher than median organization diversity can be predicted by mobilization

through online social networks with gender, income, age and race as control variables. SAS 9.2

was used to run the regression and descriptive statistics and to calculate the probability that each

coefficient is actually one.

In order to test hypothesis two, exploring the association of mobilization through online

social networks and offline protest activity, Poisson regression was used. Poisson is part of the

generalized linear model family. It is a statistical technique used when dealing with a Poisson

random variable. These random variables are usually counts of events. Typically, in the Poisson

process successful outcomes are rare. Poisson distributions are inherently skewed and the

analysis models counts of event occurrences. As the dependent variable for this model, protests,

is skewed, Poisson was used in this analysis. SAS 9.2 was used to run the regression and

descriptive statistics.

The probability mass function of the Poisson distribution is:

P(i) = e – λ λi/i!

This indicates: “the probability of observing some value or count (i) is equal to the

exponentiated value of the negative value of lambda multiplied by lambda to the ith power

divided by i factorial where i is a given value, e is the exponential constant (approximately

2.718), λ is an average rate of occurrence, and P is the Poisson probability of a specific count of i

(Kposowa 2011).”

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Estimation of the Poisson model was accomplished through a link function. In this case, a

log-linear function was used and is specified as follows:

Log µi = β0 + β 1Xi1 + β 2Xi2 + … + β kXik

The data did not meet the main assumption of Poisson regression, equidispersion, as the

variance of the dependent variable should equal its mean and the variance (155.25) of protests

exceeded the mean (7.01). This indicates the dependent variable was over dispersed. One

potential reason for over dispersion is that events are not completely independent. Using Poisson

regression with over dispersed data can lead to coefficient estimates that are inefficient and

standard errors that are biased downward. One way to correct for the over dispersion issue is to

use a negative binomial regression. This is the corrective measure that was taken in this analysis.

Negative binomial regressions maintain the Poisson structure and allow for analyses when

variances and means are not the same by introducing scale parameters and error terms. The

model for the negative binomial regression is:

Log µi = β 0 + β 1Xi1 + β 2Xi2 + … + β kXik + ei

Results

To ensure multicollinearity was not a factor in the analysis, variance inflation factors

(VIF) were examined. For this analysis multicollinearity did not appear to be a factor. No value

exceeded 2, with values ranging from 1.04 (Middle Eastern) to 1.39 (Age).

The results in Table Two support hypothesis one. The log odds of a person having above

median organizational membership diversity was significantly impacted by mobilization through

online social networks, more so than mobilization through mediated or face-to-face channels.

Specifically, attendees who were mobilized to attend the USSF through online social networks

were 91% percent more likely than attendees who were mobilized only through face-to-face

channels to have above median organizational membership diversity. In addition, with every

  21  

one-year increase in age the probability of having had above median organizational membership

diversity increased by 2.7%. To interpret the results, the unreported odds ratios were subtracted

by one and multiplied by 100.

This logistic regression model is not adequate based on Likelihood Ratio Statistic (LRS),

which is the global test statistic. The -2 log-likelihood of the model with the intercept only is

373.47. The -2 log-likelihood of the model including all of the independent variables is 350.88.

The value of the LRS is 22.59 with a p-value of 0.26, which means the global null hypothesis

could not be rejected. However, the poor global fit statistic was driven by the large number of

insignificant control variables. Only one control variable, age, was significant at the 0.05 level.

To further establish the impact of the variable online social network on organizational

membership diversity, a second model was run that only includes the control variables to test the

overall impact of the hypothesis variable. The following formula was used to judge the

additional contribution to the fit of the model (Kposowa 2000):

Change in LRS = LRS(Model 1) – LRS(Model 2)

Model 1 is the saturated model and Model 2 is the restricted model. The LRS of the restricted

model was 18.07 and the LRS change was 4.52. This test has df=1 and the p-value is 0.033,

which is significant. This confirmed the prior results based on the local test statistic.

Table two about here

The results in Table Three demonstrate support for hypothesis two. Mobilization to

attend the USSF through online social networks significantly impacts the number of offline

protests attended compared to mobilization through only face-to-face channels. Examining the

key independent variable, the expected number of offline protests attended by individuals

mobilized to attend the USSF through online social networks was 64% higher than mobilization

through only face-to-face channels. This result is unique to mobilization through online social

  22  

networks, as mediated mobilization channels did not significantly impact the expected number of

protests.

The expected number of protests attended by females was 25% less than males while the

expected number of protests attended by other gender was 46% higher than males. Also, for

every additional year of age, the expected number of protests attended increased by 2%. Personal

income generally did not have a large impact on number of protests, as most income groups are

insignificant. However, the expected number of offline protests attended by individuals who

refused to answer the question was 85% lower than those in the lowest income group. In

addition, the racial category other was significant. The expected number of offline protests was

158% higher for this group than whites. These interpretations were made using the IDR value,

which was found by taking the exponential of the parameter estimate, subtracting one from that

number and then multiplying it by 100 to turn the number into a percent.

Overall, the model fit well. The deviance divided by the degrees of freedom was less than

two (1.19). In addition, the LRS was highly significant. The log likelihood of the null model was

2110.83 while the log likelihood of the saturated model was 2143.94, which gave a log

likelihood statistic of 66.22. The statistic had a chi-square distribution with 18 degrees of

freedom because the saturated model contained 18 covariates. The p-value of the LRS is less

than 0.001 so the null hypothesis was rejected.

To further examine the impact of online social network on expected number of protests, a

null model was run that contained only the control covariates to examine the change in the LRS.

The change in log likelihood ratio was calculated using the following formula:

Change in LRS = LRS(Model 1) – LRS(Model 2)

Model 1 was the saturated model and Model 2 was the restricted model. The LRS of the

restricted model was 54.58. This was computed similarly to the saturated model’s LRS above.

  23  

The LRS change was 11.64. This test had df=1 and the p-value was less than 0.001, which was

significant. This confirms the prior results based on the local test statistic.

Table 3 about here

Discussion and Conclusion

The goal of this research was to use results from a survey fielded at the 2010 USSF to

examine whether mobilization to attend the forum through online social networks related to

organizational membership diversity and offline protest activity. In addition, online social

networking as a mobilization channel was compared to face-to-face and mediated mobilization

channels. Generally, findings support past research that show that use of the Internet increases

offline social movement engagement (della Porta and Mosca 2005, Fisher and Boekkooi 2010).

Hypothesis one proposed mobilization to attend the USSF through online social networks

impacts organizational membership diversity. Findings support results from prior research that

maintain that Internet users belong to multiple organizations or have increased levels of

organizational membership diversity (Bennett et. al. 2008, Van Laer 2010). More importantly,

this assertion can now be expanded to include not only the use of the Internet but also

specifically the use of online social networks. However, besides knowing that participants are

members of the organizations, the type of relationship or how strongly participants are embedded

in the organizations cannot be discerned from these findings and offer the opportunity for future

research. Users of online social network sites may have more flexible relationships with

organizations, which means they may have the opportunity to be involved with increased

numbers of organizations. They have the ability to learn about more events and get together with

others who support similar causes offline. The fact that mobilization through online social

networks shows increased organizational membership diversity, more so than mobilization

through face-to-face channels, could mean online social network users have access to myriad

  24  

personal contacts. This supports the idea that use of the Internet, “enables the organization of

networks operating beyond the reach of formal organizations” (Bennett et. al. 2008; 273). The

Internet and specifically online social networks provide an ideal backdrop for movements like

the GSJ movement, which encapsulates and supports the inclusion of diverse topics and

networks of individuals.

Hypothesis two examined the relationship between mobilization through online social

networks and a specific degree of movement activity, number of protests attended in a year.

Results support past findings indicating Internet users are more likely to have protested in the

past (della Porta and Mosca 2005; Van Laer 2010). Results also support the assertion that the

Internet supplements other forms of offline interaction (Polat 2005). One benefit of the Internet

is the facilitation of communication and interaction across different networks. This increases the

chance that participants might be asked to take part in social movement activity (Van Laer 2010).

Results show that men are more likely to protest than women. This research allows a better

understanding of mobilization through online social networks versus face-to-face channels. In

addition, results from both models help support the notion of the strength of weak ties

(Granovetter 1983; Kavanaugh, Reese, Carroll, and Rosson 2005).

More broadly, the implications of this research support the notion that online social

networks matter in facilitating social change. As depicted by the results of this study and in the

discussions surrounding the role of online social networks in the Middle East revolutions, there

are myriad implicit and explicit effects of online social networks that influence the organization

and mobilization of social movement activity (Zhuo, et al. 2011). While not taking the place of

more traditional forms of communication, the role of online social networks needs to be

considered when examining social movement communication and mobilization. Practically, for

members of social movements, activists should add online social networks to the repertoire of

  25  

more traditional outlets they have available to them, such as face-to-face and mediated channels,

as they strive to pursuit their movement goals.

This research does have its limitations. Fielding surveys at events such as the USSF is

challenging. Therefore, the USSF sample results in limitations to the study as attendees at the

USSF may not be the same as typical activists. Activity at the USSF, an event specifically

developed to be a non-hierarchical, participatory environment and created under the ideology of

the GSJ movement, may not be transferrable to other social movement events. In addition, the

respondents were largely U.S. based. It would be interesting to see if similar results would be

found in other parts of the world.

Future research could explore the nuances of the relationships between online social

network users and organizations such as their positions in organizations. Future research could

also examine the particular online tools and technologies that people use, such as Twitter and

Facebook, and their influence on offline activities. Moving away from survey work, future

research could also use more qualitative methods, such as interviews or ethnography, to obtain a

better understanding as to how social movement activities make use of online social networks

and which mechanisms lead to the use of online social networks. Research could also explore if

certain kinds of online activism using particular online social networks spurs specific offline

activity. Overall, these findings help reveal the importance the Internet plays, and will continue

to play, in social movement activity. Continued research is needed to explore the ways that

online social networks influence social movements.

  26  

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Table 1. Descriptive Statistics for Organizational Membership Diversity, Protests, and Online Social Networks

Variables N Std Dev

Mean Skewness Min Max

Protests 500 12.46 7.01 5.34 0 115 Organizational Membership Diversity

High 160 0.46 0.30 0.85 0 1 Low 290 0.50 0.51 -0.06 0 1 Mobilization channel Online social networks 134 0.43 0.24 1.23 0 1 Mediated 229 0.49 0.41 0.38 0 1 Face-to-face 564 0.32 0.88 -2.36 0 1 Age 509 16.57 36.47 0.97 18 93 Gender Female 299 0.50 0.56 -0.24 0 1 Male 218 0.49 0.41 0.37 0 1 Other 17 0.18 0.03 5.35 0 1 Personal income None - $14,999 166 0.50 0.43 0.28 0 1 $15,000 - $20,999 46 0.32 0.12 2.36 0 1 $21,000 - $39,999 78 0.40 0.20 1.49 0 1 $40,000 - $51,999 31 0.27 0.08 3.10 0 1 $52,000 - $63,999 16 0.20 0.04 4.62 0 1 $64,000 or Above 20 0.23 0.05 3.92 0 1 Refused 7 0.14 0.02 7.03 0 1 Race White 281 0.50 0.55 -0.21 0 1 Latino/Hispanic 73 0.35 0.14 2.04 0 1 Black 54 0.31 0.11 2.56 0 1 Multiracial 47 0.29 0.09 2.82 0 1 Asian 26 0.22 0.05 4.09 0 1 Middle Eastern 4 0.09 0.01 11.17 0 1 Indigenous 4 0.09 0.01 11.17 0 1 Other 16 0.17 0.03 5.38 0 1

  33  

Table 2. Logistic Regression Analysis Results of the Effects of Mobilization Through Online Social Networks on Organizational Membership Diversity.

Model 1 Model 2 Mobilization channel Face-to-face -.572 (.426) Mediated .096 (.282) Online social networks .647 * (.303) Age .024 * .027 ** (.010) (.010) Gender Male ---- ---- Female .372 .410 (.276) (.280) Other .536 .534 (.634) (.647) Personal income None - $14,999 ------- ------- $15,000 - $20,999 -.581 -.593 (.421) (.432) $21,000 - $39,999 -.412 -.386 (.350) (.354) $40,000 - $51,999 -.933 -.917 (.549) (.555) $52,000 - $63,999 -.635 -.594 (.678) (.689) $64,000 or Above -.426 -.356 (.652) (.678) Refused -1.679 -1.633 (1.160) (1.182)

  34  

Race White ---- ---- Latino/Hispanic .192 .214 (.407) (.415) Black -.158 -.113 (.487) (.501) Multiracial -.124 -.163 (.461) (.474) Asian -.288 -.293 (.704) (.72) Middle Eastern -13.457 -13.163 (770.400) (774.000) Indigenous 1.066 0.977 (1.476) (1.527) Other .224 .257 (.816) (.847) Intercept -1.594

(.407)

***

-1.444 (.637)

*

R-squared 0.065 0.101 Sample Size 305 305 Notes: Numbers in parentheses are standard errors. *p<.05; **p<.01; ***p<.001 (two-tailed test).

  35  

Table 3. Negative Binomial Regression Results for Protests and Online Social Networks

Model 1 Model 2 Mobilization channel Face-to-face .392 (.233) Mediated .096 (.135) Online social networks .496 *** (.147) Age .019 *** .022 *** (.005) (.005) Gender Male ---- ---- Female -.300 * -.291 * (.133) (.131) Other .360 .380 (.331) (.323) Personal income None - $14,999 -------- -------- $15,000 - $20,999 -.118 -.131 (.203) (.198) $21,000 - $39,999 -.019 -.001 (.169) (.167) $40,000 - $51,999 .104 .120 (.229) (.226) $52,000 - $63,999 -.142 -.120 (.321) (.320) $64,000 or Above -.353 -.197 (.328) (.324) Refused -1.978 ** -1.920 ** (.739) (.732)

  36  

Race White ---- ---- Latino/Hispanic .237 .281 (.206) (.202) Black -.289 -.240 (.246) (.249) Multiracial .357 .528 (.219) (.228) Asian .169 .428 (.356) (.354) Middle Eastern -1.308 -1.064 (.814) (.805) Indigenous -.077 -.178 (.790) (.788) Other .877 * .946 ** (.373) (.366) Intercept 1.205

(.194)

***

1.205 (.194)

***

Sample Size 295 295